{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "使用LDA, 分析yelp数据集"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>business_id</th>\n",
       "      <th>date</th>\n",
       "      <th>review_id</th>\n",
       "      <th>stars</th>\n",
       "      <th>text</th>\n",
       "      <th>type</th>\n",
       "      <th>user_id</th>\n",
       "      <th>cool</th>\n",
       "      <th>useful</th>\n",
       "      <th>funny</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>9yKzy9PApeiPPOUJEtnvkg</td>\n",
       "      <td>2011-01-26</td>\n",
       "      <td>fWKvX83p0-ka4JS3dc6E5A</td>\n",
       "      <td>5</td>\n",
       "      <td>My wife took me here on my birthday for breakf...</td>\n",
       "      <td>review</td>\n",
       "      <td>rLtl8ZkDX5vH5nAx9C3q5Q</td>\n",
       "      <td>2</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>ZRJwVLyzEJq1VAihDhYiow</td>\n",
       "      <td>2011-07-27</td>\n",
       "      <td>IjZ33sJrzXqU-0X6U8NwyA</td>\n",
       "      <td>5</td>\n",
       "      <td>I have no idea why some people give bad review...</td>\n",
       "      <td>review</td>\n",
       "      <td>0a2KyEL0d3Yb1V6aivbIuQ</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>6oRAC4uyJCsJl1X0WZpVSA</td>\n",
       "      <td>2012-06-14</td>\n",
       "      <td>IESLBzqUCLdSzSqm0eCSxQ</td>\n",
       "      <td>4</td>\n",
       "      <td>love the gyro plate. Rice is so good and I als...</td>\n",
       "      <td>review</td>\n",
       "      <td>0hT2KtfLiobPvh6cDC8JQg</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>_1QQZuf4zZOyFCvXc0o6Vg</td>\n",
       "      <td>2010-05-27</td>\n",
       "      <td>G-WvGaISbqqaMHlNnByodA</td>\n",
       "      <td>5</td>\n",
       "      <td>Rosie, Dakota, and I LOVE Chaparral Dog Park!!...</td>\n",
       "      <td>review</td>\n",
       "      <td>uZetl9T0NcROGOyFfughhg</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>6ozycU1RpktNG2-1BroVtw</td>\n",
       "      <td>2012-01-05</td>\n",
       "      <td>1uJFq2r5QfJG_6ExMRCaGw</td>\n",
       "      <td>5</td>\n",
       "      <td>General Manager Scott Petello is a good egg!!!...</td>\n",
       "      <td>review</td>\n",
       "      <td>vYmM4KTsC8ZfQBg-j5MWkw</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              business_id        date  ...   useful  funny\n",
       "0  9yKzy9PApeiPPOUJEtnvkg  2011-01-26  ...        5      0\n",
       "1  ZRJwVLyzEJq1VAihDhYiow  2011-07-27  ...        0      0\n",
       "2  6oRAC4uyJCsJl1X0WZpVSA  2012-06-14  ...        1      0\n",
       "3  _1QQZuf4zZOyFCvXc0o6Vg  2010-05-27  ...        2      0\n",
       "4  6ozycU1RpktNG2-1BroVtw  2012-01-05  ...        0      0\n",
       "\n",
       "[5 rows x 10 columns]"
      ]
     },
     "execution_count": 1,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pandas as pd \n",
    "\n",
    "yelp = pd.read_csv('./data/yelp.csv', encoding='utf-8')\n",
    "yelp.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.feature_extraction.text import CountVectorizer, TfidfVectorizer\n",
    "from sklearn.decomposition import LatentDirichletAllocation\n",
    "\n",
    "# 把每个文本表示成count vector. \n",
    "vectorizer = CountVectorizer(stop_words='english')\n",
    "X = vectorizer.fit_transform(yelp['text'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.decomposition import LatentDirichletAllocation\n",
    "from sklearn.feature_extraction.text import CountVectorizer, TfidfVectorizer\n",
    "\n",
    "\n",
    "# 把每个文本表示成count vector. \n",
    "vectorizer = CountVectorizer(stop_words=\"english\")\n",
    "X = vectorizer.fit_transform(yelp['text'])\n",
    "\n",
    "# 只是使用下LDA，聚出来的类没啥实际意义\n",
    "lda = LatentDirichletAllocation(n_topics=10, learning_method='batch', random_state=9)\n",
    "model = lda.fit(X)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x720 with 10 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "\n",
    "####\n",
    "# Per topic: (token, pseudocount)\n",
    "# pseudocount represents the number of times word j was assigned to topic i\n",
    "# \n",
    "# We can to convert these to a normalized form -- well you don't have to\n",
    "# but it's easier to understand the output in this form.  Also, this is \n",
    "# consistent with how Gensim performs.  After some consideration, we will plot these out.\n",
    "####\n",
    "def display_topics(model, feature_names, no_words = 10, plot = False, plot_dim=(5,2)):\n",
    "    \n",
    "    topics_tokens = []\n",
    "    \n",
    "    for topic_idx, topic in enumerate(model.components_):\n",
    "\n",
    "        topic = zip(feature_names, topic)\n",
    "        topic = sorted(topic, key=lambda pair: pair[1])\n",
    "        \n",
    "        topic_words = [(token, counts)\n",
    "                       for token, counts in topic[:-no_words - 1:-1]]\n",
    "        \n",
    "        topics_tokens.append(topic_words)\n",
    "        \n",
    "        if not plot:\n",
    "            print (\"Topic %d:\" % (topic_idx))\n",
    "            print (topic_words)\n",
    "        \n",
    "    if plot:\n",
    "    \n",
    "        fig, ax = plt.subplots(figsize=(10, 10), nrows=5, ncols=2)\n",
    "        \n",
    "        topics = [\n",
    "            {key: value for key, value in topic} \n",
    "                  for topic in topics_tokens\n",
    "        ]\n",
    "        \n",
    "        row = 0\n",
    "        \n",
    "        for topic_id, topic in enumerate(topics):\n",
    "            \n",
    "            column = (0 if topic_id % 2 == 0 else 1)\n",
    "                \n",
    "            chart = pd.DataFrame([topic]).iloc[0].sort_values(axis=0)\n",
    "            chart.plot(kind=\"barh\", title=\"Topic %d\" % topic_id, ax=ax[row, column])\n",
    "                \n",
    "            row += 1 if column == 1 else 0\n",
    "        \n",
    "        plt.tight_layout()\n",
    "            \n",
    "\n",
    "display_topics(model, vectorizer.get_feature_names(), no_words=10, plot=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Topic 0:\n",
      "[('place', 739.447921708801), ('like', 708.9472285648228), ('just', 431.31261051979754), ('really', 376.1428623082056), ('ve', 349.09434528656834), ('time', 326.02197661980694), ('good', 323.2653100993834), ('don', 277.7761456385078), ('love', 250.86295326653027), ('sandwich', 236.6507243814213)]\n",
      "Topic 1:\n",
      "[('breakfast', 629.455019622789), ('pizza', 397.8103735599957), ('good', 278.5848232908661), ('place', 274.484258419919), ('little', 261.8400752464567), ('ve', 259.2419354809493), ('eggs', 225.68360228508618), ('just', 224.11033404664875), ('best', 221.1683972594351), ('like', 197.21928186888493)]\n",
      "Topic 2:\n",
      "[('food', 2090.473513524268), ('place', 1443.275748621843), ('good', 1367.001029981974), ('just', 1181.6559068919264), ('time', 1074.7742558979976), ('service', 1027.994502082337), ('like', 873.9175643316521), ('really', 721.3277532053473), ('bar', 691.6946817091825), ('great', 681.8453878188777)]\n",
      "Topic 3:\n",
      "[('park', 146.66366609896147), ('parking', 140.67563262614863), ('gym', 113.01206662952374), ('nice', 109.8659293905423), ('classes', 92.30746458787841), ('people', 89.65944269692159), ('pretty', 83.31613812718103), ('phoenix', 82.18193593338674), ('clean', 82.09173722843579), ('room', 80.36853849355983)]\n",
      "Topic 4:\n",
      "[('ice', 145.84708605840544), ('just', 145.29517561414443), ('like', 129.58856056505567), ('cream', 125.41783767462609), ('coffee', 124.25008993237024), ('place', 123.92480639466558), ('don', 91.51325260923355), ('want', 91.03427876851583), ('shop', 89.31260308762731), ('people', 85.20080880388552)]\n",
      "Topic 5:\n",
      "[('sushi', 623.0045753976485), ('coffee', 305.3925937175543), ('great', 208.26347014437474), ('roll', 191.58163269269855), ('best', 175.1240650643986), ('like', 157.06986184036), ('just', 129.93936248301793), ('love', 121.6634213610273), ('ve', 120.26942075728898), ('tea', 119.75918752101117)]\n",
      "Topic 6:\n",
      "[('food', 2724.4490483450722), ('good', 2620.0945920917184), ('place', 1741.8894133946371), ('chicken', 1388.3060328068154), ('like', 1380.1955275086189), ('great', 1212.4742000660367), ('just', 952.5433541097973), ('sauce', 937.1345504586994), ('salad', 913.4991358064376), ('restaurant', 902.956597675511)]\n",
      "Topic 7:\n",
      "[('time', 442.1022335768219), ('service', 356.4212190543435), ('did', 329.1990208492327), ('room', 303.84749559377786), ('like', 288.61861406545296), ('great', 277.8305698095705), ('just', 273.7059541936242), ('car', 253.1558771273888), ('nice', 242.32146313549242), ('hotel', 239.59458879107854)]\n",
      "Topic 8:\n",
      "[('great', 1972.8073384836775), ('good', 1600.1992893266745), ('place', 1504.2287994215105), ('food', 846.8119865138632), ('love', 841.3660388826321), ('just', 806.6935690182894), ('burger', 730.6162581145076), ('like', 717.5345964967968), ('really', 672.5749803256007), ('pizza', 661.6818806828351)]\n",
      "Topic 9:\n",
      "[('place', 549.0693823351482), ('like', 510.49703103447104), ('store', 468.85004381348955), ('great', 436.79812921972285), ('just', 369.82734797791045), ('don', 296.4616270492848), ('prices', 265.7041303359205), ('really', 264.9288058154078), ('good', 237.658046409146), ('people', 221.65322082412092)]\n"
     ]
    }
   ],
   "source": [
    "display_topics(model, vectorizer.get_feature_names(), no_words=10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>topic 0</th>\n",
       "      <th>topic 1</th>\n",
       "      <th>topic 2</th>\n",
       "      <th>topic 3</th>\n",
       "      <th>topic 4</th>\n",
       "      <th>topic 5</th>\n",
       "      <th>topic 6</th>\n",
       "      <th>topic 7</th>\n",
       "      <th>topic 8</th>\n",
       "      <th>topic 9</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>0.871409</td>\n",
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       "      <td>0.014289</td>\n",
       "      <td>0.014286</td>\n",
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       "      <td>0.014289</td>\n",
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       "    <tr>\n",
       "      <th>79</th>\n",
       "      <td>0.930750</td>\n",
       "      <td>0.007696</td>\n",
       "      <td>0.007695</td>\n",
       "      <td>0.007694</td>\n",
       "      <td>0.007694</td>\n",
       "      <td>0.007694</td>\n",
       "      <td>0.007694</td>\n",
       "      <td>0.007694</td>\n",
       "      <td>0.007694</td>\n",
       "      <td>0.007695</td>\n",
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       "    <tr>\n",
       "      <th>162</th>\n",
       "      <td>0.954985</td>\n",
       "      <td>0.005003</td>\n",
       "      <td>0.005002</td>\n",
       "      <td>0.005001</td>\n",
       "      <td>0.005001</td>\n",
       "      <td>0.005001</td>\n",
       "      <td>0.005005</td>\n",
       "      <td>0.005001</td>\n",
       "      <td>0.005001</td>\n",
       "      <td>0.005001</td>\n",
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       "    <tr>\n",
       "      <th>180</th>\n",
       "      <td>0.801063</td>\n",
       "      <td>0.001163</td>\n",
       "      <td>0.001163</td>\n",
       "      <td>0.001163</td>\n",
       "      <td>0.001163</td>\n",
       "      <td>0.001163</td>\n",
       "      <td>0.001163</td>\n",
       "      <td>0.001163</td>\n",
       "      <td>0.189632</td>\n",
       "      <td>0.001163</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>190</th>\n",
       "      <td>0.968958</td>\n",
       "      <td>0.003449</td>\n",
       "      <td>0.003450</td>\n",
       "      <td>0.003449</td>\n",
       "      <td>0.003449</td>\n",
       "      <td>0.003449</td>\n",
       "      <td>0.003449</td>\n",
       "      <td>0.003449</td>\n",
       "      <td>0.003449</td>\n",
       "      <td>0.003449</td>\n",
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       "  </tbody>\n",
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       "</div>"
      ],
      "text/plain": [
       "      topic 0   topic 1   topic 2    ...      topic 7   topic 8   topic 9\n",
       "21   0.871409  0.014287  0.014289    ...     0.014288  0.014288  0.014289\n",
       "79   0.930750  0.007696  0.007695    ...     0.007694  0.007694  0.007695\n",
       "162  0.954985  0.005003  0.005002    ...     0.005001  0.005001  0.005001\n",
       "180  0.801063  0.001163  0.001163    ...     0.001163  0.189632  0.001163\n",
       "190  0.968958  0.003449  0.003450    ...     0.003449  0.003449  0.003449\n",
       "\n",
       "[5 rows x 10 columns]"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "comp = model.transform(X)\n",
    "document_topics = pd.DataFrame(comp, columns=[\"topic %d\" % i for i in range(comp.shape[1])])\n",
    "top_topics = document_topics['topic 0'] > 0.8\n",
    "document_topics[top_topics].head()"
   ]
  }
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